Space dataset optimization

ABSTRACT

Disclosed method comprises receiving from a user interface, a request to generate an optimized space dataset; generating a first instruction configured to receive a first dataset comprising at least one of a space allocated to the product, product attribute, and product performance value corresponding to a plurality of institutions; automatically calculating an elasticity range based on the space allocated to the product; calculating an unbounded asymptote and elasticity values; upon the unbounded elasticity value being outside the elasticity range, dynamically adjusting the elasticity value based on the elasticity range; iteratively calculating a bounded asymptote value based on the second dataset data points until the asymptote value is within the adjusted elasticity range; and generating a graphical representation to illustrate an optimized space trend comprising plurality of data points.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 15/653,242, filed Jul. 18, 2017, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

This application relates generally to generating and manipulatingdatasets associated with efficient analytics processing.

BACKGROUND

Institutions desire to learn about an initiative's effectiveness. Theinstitutions will continuously try to optimize the store layouts andarrangements in order to maximize their profit and predict customerperformance given different scenarios. The institutions often attempt tomaximize their sales and profit by optimizing the space within the storededicated to certain commodities. In one conventional example, aretailer owns multiple stores that sell a variety of products.Conventional theories posit that a store will sell more commodities ifthe store displays more of that commodity (e.g., the store dedicatesmore space to said commodity). However, the concept of diminishingreturn explains that blindly increasing the space dedicated to a productis not necessarily the best solution to maximize profits. Thediminishing return concept is the decrease in the marginal (incremental)output of a production process as the amount of a single factor ofproduction is incrementally increased, while the amounts of all otherfactors of production stay constant. For example, continuing with theexample above, if the retailer increases the space from 5% of theshelving units to 10%, the store may see a 100% increase in sales (e.g.,sales may double); however, if the same retailer increases the spacededicated to the same product to 20% of all the shelving units, thestore may not experience a 400% increase in sales (e.g., sales may notquadruple). Simply put, the law of diminishing returns states that inall productive processes, increasing one factor of production, whileholding all others constant (“ceteris paribus”), will at some point(e.g., diminishing return point) yield lower incremental per-unitreturns.

Conventional approaches to optimizing a space within a store based oncustomer behavior have been accomplished using a “trial-and-error”method of modifying the spaces and studying customer behavior utilizing“brute force” methods, such as analyzing sales in relation to the spaceallocated to products. For example, an institution may allocate morespace to a product, analyze the sales associated with said product for apre-determined period of time, and depending on the analysis, change thespace allocated to the product and re-analyze the sales. As expected,this process is tedious and time consuming. The “trial-and-error” methodis also inaccurate because many other factors associated with sales(e.g., seasonality, utility, or demand) may change throughout theanalysis, which may yield unexpected and imprecise results. Furthermore,the “trial-and-error” method may not be suitable because it heavilyrelies on human subjectivity (e.g., the amount of space and/or the priceare selected by the analyzers).

As the processing power of computers allow for greater computerfunctionality and the Internet technology era allows forinterconnectivity between computing systems, many institutions usecomputers to optimize retail space. However, since the implementation ofthese more sophisticated online tools, several shortcomings in thesetechnologies have been identified and have created a new set oftechnical challenges. Several existing and conventional softwaresolutions provide the same “trial-and-error” method implemented oncomputing devices and fail to provide fast and efficient analysis due toa high volume of customer/store information existing on differentnetworks and computing infrastructures. Managing such information ondifferent platforms is difficult due to number, size, content, orrelationships of the data associated with the customers. For example,optimizing space for a store that provides several products may entailcalculating millions or billions of different and distinct combinationsof sales and space. Conventional software solutions may take hours oreven days to complete the analysis because there is often not enoughprocessing power and time to search and analyze all differentcombination of the spaces, sales prices, and diminishing return valuesallocated to each product. Furthermore, many existing and conventionalgraphical user interfaces do not illustrate the optimized data (e.g.,optimized spaces and projected sales trends) in a user-friendly manner.For example, conventional software solutions may produce largespreadsheets or large graphs comprising confusing data points.

SUMMARY

For the aforementioned reasons, there is a need for a more efficient andfaster system, method, and a software solution for processing largespace and sales datasets, which would allow institutions to optimize thespace allocated to different products and study customer behavior (e.g.,sales or other customer behavior) in a more efficient manner thanpossible with human-intervention or conventional computer data-drivenanalysis. There is a need for a network and computer-specific set ofrules to produce efficient and accurate results when facing a highnumber of space and sales combinations. These features can performtime-consuming analysis in a more efficient manner and can generatecustom control group datasets in a more efficient manner by using lesscomputing power than other approaches, such as conventional softwaresolutions. There is also a need for a more user-friendly graphical userinterface to illustrate optimization trends and projections.

In one embodiment, a method comprises receiving, by a server from a userinterface, a request to generate an optimized space dataset, wherein thereceived request comprises identification associated with a product anda first institution; generating, by the server, a first instructionconfigured to receive a first dataset comprising at least one of a spaceallocated to the product, product attribute, and product sales valuecorresponding to a plurality of institutions, wherein the plurality ofinstitutions comprises at least the first institution; upon transmittingthe first instruction to a first database, receiving, by the server, thefirst dataset; identifying, by the server, a plurality of first datasetdata points, wherein each first dataset data point corresponds to thespace allocated to the product and the product sales value associatedwith the plurality of institutions; automatically calculating, by theserver, an elasticity range based on the space allocated to the product,wherein the elasticity range represents a maximum elasticity range valueand a minimum range elasticity value each representing maximum andminimum product space value associated with the plurality ofinstitutions; determining, by the server, an unbounded elasticity valueand a corresponding asymptote value based on a second dataset, whereinthe second dataset comprises second dataset data points corresponding tospace allocated to the product and the product performance valueassociated with the first institution; in response to the unboundedelasticity value being outside the elasticity range, dynamicallyadjusting, by the server, the elasticity value based on the elasticityrange; iteratively calculating, by the server, a bounded asymptote valueand a bounded elasticity value based on the second dataset data pointsuntil the bounded asymptote and elasticity values are within theadjusted elasticity range, wherein a number of iterative calculations isbased on the adjusted elasticity range and an incremental value;generating, by the server, a graphical representation of the seconddataset data points, wherein the graphical representation is configuredto illustrate an optimized space trend comprising plurality of datapoints; and generating, by the server, a second instruction configuredto display plurality of second set data points on the user interface.

In another embodiment, a computer system comprising a server, which isconfigured to receive, from a user interface, a request to generate anoptimized space dataset, wherein the received request comprisesidentification associated with a product and a first institution;generate a first instruction configured to receive a first datasetcomprising at least one of a space allocated to the product, productattribute, and product sales value corresponding to a plurality ofinstitutions, wherein the plurality of institutions comprises at leastthe first institution; upon transmitting the first instruction to afirst database, receive the first dataset; identify a plurality of firstdataset data points, wherein each first dataset data point correspondsto the space allocated to the product and the product sales valueassociated with the plurality of institutions; automatically calculatean elasticity range based on the space allocated to the product, whereinthe elasticity range represents a maximum elasticity range value and aminimum range elasticity value each representing maximum and minimumproduct space value associated with the plurality of institutions;determine an unbounded elasticity value and a corresponding asymptotevalue based on a second dataset, wherein the second dataset comprisessecond dataset data points corresponding to space allocated to theproduct and the product performance value associated with the firstinstitution; in response to the unbounded elasticity value being outsidethe elasticity range, dynamically adjusting, by the server, theelasticity value based on the elasticity range; iteratively calculate abounded asymptote value and a bounded elasticity value based on thesecond dataset data points until the bounded asymptote and elasticityvalues are within the adjusted elasticity range, wherein a number ofiterative calculations is based on the adjusted elasticity range and anincremental value; generate a graphical representation of the seconddataset data points, wherein the graphical representation is configuredto illustrate an optimized space trend comprising plurality of datapoints; and generate a second instruction configured to displayplurality of second set data points on the user interface.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory and areintended to provide further explanation of the invention as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings constitute a part of this specification andillustrate an embodiment of the invention and together with thespecification, explain the invention.

FIG. 1 illustrates an example of a system for generating an optimizedspace dataset, according to an embodiment.

FIG. 2 illustrates a flowchart depicting operational steps of a methodfor generating an optimized space dataset, according to an embodiment.

FIG. 3 illustrates distribution of sales in relation to space datapoints, according to an embodiment.

FIG. 4 illustrates distribution of sales in relation to space datapoints, according to an embodiment.

FIG. 5 illustrates distribution of sales in relation to space datapoints, according to an embodiment.

DETAILED DESCRIPTION

Reference will now be made to the exemplary embodiments illustrated inthe drawings, and specific language will be used here to describe thesame. It will nevertheless be understood that no limitation of the scopeof the invention is thereby intended. Alterations and furthermodifications of the inventive features illustrated here, and additionalapplications of the principles of the inventions as illustrated here,which would occur to a person skilled in the relevant art and havingpossession of this disclosure, are to be considered within the scope ofthe invention.

FIG. 1 illustrates components of a system 100, according to an exemplaryembodiment. The system 100 comprises an analytics server 110, a databaseserver 140 a and a database 140 b of a database service provider, and aclient device 120. The above-mentioned computing devices may communicatewith each other via the communication network 130, such as the Internet.In operation, the client device 120 may request the analytics server 110to derive various forms of analytical information from the data recordsof the database service provider. The database 140 b, associated withthe database service provider, may store data records that areassociated with customer interactions where the data records eachcontain at least one field identifying which customer or customeraccount was associated with a particular interaction. For example, thedatabase 140 b may record the sales figures and other pertinentinformation (e.g., time of purchase, the location of the product inrelation to other products, sales volume, product pricing trend,frequency of customer interactions) customer attributes (e.g., tenure,age, purchase power, demographics data) and the like. The database 140 bmay store data records generated and stored by a database server 140 aduring customer interactions at one or more stores associated with aninstitution and/or the data service provider. The database 140 b may behosted on any number of computing devices comprising a non-transitorymachine-readable storage medium capable of storing data records receivedfrom the database server 140 a, and in some cases, received from theanalytics server 110, the client device 120, or other computing devices(e.g., point-of-sale systems, public websites, and the like). Thedatabase 140 b may further comprise a processor capable of executingvarious queries and data record management processes, according toinstructions from the analytics server 110 or the database server 140 a.One skilled in the art would appreciate that the database 140 b may bethe same computing device as the database server 140 a or be hosted on adistinct computing device that is in networked-communication with theanalytics server 110.

The analytics server 110 may perform various analytics on data recordsstored in the database 140 b and transmit the results to the clientdevice 120. The analytics server 110 may be any computing devicecomprising a processor capable of performing the various tasks andprocesses described herein. Non-limiting examples of the analyticsserver 110 may include a server, desktop, laptop, tablet, and the like.The analytics server 110 comprises any number of computer-networkingcomponents that facilitate inter-device communications via thecommunication network 130. One skilled in the art would appreciate thatthere may be any number of distinct computing devices functioning as theanalytics server 110 in a distributed computing environment.

The database server 140 a may communicate data records and instructionsto and from the analytics server 110, where the data records may bestored into the database 140 b and where various analytics may beperformed on the data by the database server 140 a in accordance withthe instructions from the analytics server 110 and/or the client device120. The database server 140 a may be any computing device comprising aprocessor capable of performing the various tasks and processesdescribed herein. Non-limiting examples of a database server 140 a mayinclude a server, desktop, laptop, tablet, and the like. The databaseserver 140 a comprises any number of computer-networking components(e.g., network interface card) that facilitate inter-devicecommunications via the communication network 130. One having skill inthe art would appreciate that there may be any number of distinctcomputing devices functioning as the database server 140 a in adistributed computing environment.

The client device 120 may access a web-based service or applicationhosted by a database server 140 a, from which customers may provide orrequest various types of personal and/or confidential data that may bestored in the database 140 b. The client device 120 may be any computingdevice comprising a processor capable of performing the various tasksand processes described herein. Non-limiting examples of a client device120 may include a server, desktop, laptop, tablet, and the like. Theclient device 120 comprises any number of computer-networking components(e.g., network interface card) that facilitate inter-devicecommunications via the communication network 130.

Referring now to FIG. 2, a flowchart depicting operational steps of amethod for optimizing a space dataset is illustrated in accordance withan embodiment. Steps of the method 200 may be implemented using one ormore software modules executed by the analytics server, the clientdevice, and/or the database service provider. FIG. 2 does not imply anylimitations with regard to the environments or embodiments that may beimplemented. Modifications to the depicted environment or embodimentshown in FIG. 2 may be made. While certain aspects may be illustratedherein with reference to optimizing space in relation to sales figureswithin a store, it is expressly understood that these embodiments can beconfigured to apply to a variety of other optimization services, such asoptimization of any performance related to space.

At step 210, the analytics server may receive a request to generate anoptimized space dataset. In some embodiments, the analytics server mayreceive this request from a client interacting with a user interfaceassociated with the client device. For example, the analytics server maypresent a user interface (e.g., a website provided to the user operatingthe client computing device) configured to receive information relatedto the client and the client request. In other embodiments, the clientrequest may be electronically inputted or transmitted to the analyticsserver using the communication network. The client request may comprisea request to analyze past performance (e.g., sales and space) of one ormore stores within an enterprise and generate an optimized space datasetfor a specific item a particular store (or multiple stores). The clientrequest may further comprise information about the item to be analyzed.For example, the client request may comprise a request to generate anoptimized space dataset for product X, which is sold in 150 storesnationwide. The client interacting with a user interface associated withthe client device may be a client who is a business owner and would liketo identify the best possible amount of space allocation to the productX within one or more stores nationwide.

At step 220, the analytics server may generate an instruction to receivesales and space datasets from the database service provider. The spaceand sales datasets may refer to all the information pertinent to productX's sales and space associated with each store within the enterprise.The database service provider, which is in communication with each storewithin the enterprise and associated with the client device, may collectrecords of attributes associated with product X within every store thatoffers product X (e.g., the space allocated to the product, the salesvolume and amount, and the profit associated with product X). Theanalytics server may generate the instruction based on the clientrequest received from the client device. The analytics server maytransmit the instruction to the database server of the database serviceprovider or any other server associated with the enterprise.

Upon transmittal of the instruction to the database server, at step 230,the analytics server may receive the sales and space datasets comprisingall the customer information, and other pertinent information indicatedwithin the client request. In some other embodiments, all the pertinentinformation regarding the test strategy may be received from the clientrequest. For example, product X may only be provided by a small numberof stores within the enterprise and the client, operating the userinterface provided by the analytics server, may input the sales andspace data associated with product X within all said stores.

At step 240, the analytics server may identify data points within thesales and space dataset. In some embodiments, the data points may referto different attributes associated with product X (e.g., space allocatedto product X within each store, the price, and the sales volume of theproduct X). In some embodiments, the sales and space dataset maycomprise derivative information regarding product X (e.g., salesmargin). A data point may refer to a simple coordinate, whichcorresponds to product attributes and sales volume. For example, theanalytics server may organize the data points within the space and timedataset based on the space and sales attributes. In some embodiments,the analytics server may represent the organized data utilizing agraphic chart, such as the charts depicted in FIG. 3 and FIG. 4. Theanalytics server may also generate an instruction to display the graphon the client device or the user interface associated with the clientdevice. This graphical organization may be used to show a distributionof products and the corresponding sales figures given different spaceswithin each store.

FIG. 3 illustrates distribution of sales in relation to the spaceallotted within a store, according to an embodiment. The X-axis in thegraph illustrated in FIG. 3 represents the space allotted to product Xand the Y-axis represents sales volume associated with product X. Theanalytics server may organize the data points received in step 240 andgraphically represent them on the user interface on the client device.For example, data point 320 may represent the sales and space associatedwith product X within store B. Data point 320 comprises an X coordinateand a Y coordinate. In this embodiment, data point 320 represents thatproduct X has been allotted 6 feet of shelve space within store B andhas an $8000 sales volume.

At step 250, analytics server may calculate elasticity limit values.Elasticity, as used herein, refers to how fast or slow a graph (e.g.,graph 310 or 410) reaches its asymptote. An asymptote is a line or acurve such that the distance between the curve/line approaches zero asthe function approaches infinity. As used herein, asymptote is a linethat indicates a sales value as the space reaches a large value (e.g.,in theory, infinity). For example, consistent with the theory ofdiminishing return, as the space allocated to a particular productincreases, the sales also increase up to a point (e.g., point ofdiminishing return); after the point of diminishing return, the salesmay increase or decrease at a disproportionate rate. An asymptote is aline, which denotes no increase in sales when given an increase inspace. For example, and referring to FIG. 4, data point 430 is thediminishing return data point or the beginning of the asymptote line,which means that any data point with a larger value than data point 430on the X-axis (e.g., space) may not have a larger sales value. Forexample, data points 430, 420, 450, 460 respectively represent 0.25 ft,0.75 ft, 1 ft, and 1.5 ft of space allocated to the same product withinthe same store. However, the sales volume associated with each of thedata points remain unchanged (e.g., $500). In other words, graph 410represents that even if the space allocated to product X increases from0.25 ft. to 1 ft. or 1.5 ft., there will be no change in the salesvolume. An elasticity value represents the distance (on the X-axis orthe space) between the asymptote and the beginning of the graph. Forexample, in FIG. 4, distance 440 represents the elasticity of asymptote410. A small elasticity value, such as the distance 440, may representthat a graph rapidly reaches its asymptote (e.g., the point ofdiminishing return is reached quickly). In contrast, a large elasticityvalue may represent that a graph will not reach its asymptote until avery large value of space is allocated to the product (e.g., the pointof diminishing return is slowly reached). Function 310 of FIG. 3represents a graph with a large elasticity. Function 310 is a slopedline and does not have a visible asymptote (e.g., the asymptote is notwithin the normal range of spaces within a store). As discussed above,both functions 310 and 410 represent unrealistic and undesirable resultsbecause they do not provide realistic space optimization within thephysical space constraint of any store. The analytics server may try tolimit the elasticity value in order to reach a more realistic and/ordesirable representation of the sales and space.

The analytics server may calculate the elasticity limit values (e.g.,upper and lower bounds) by analyzing data from all the stores within theenterprise. For example, all the stores that provide similar servicesmay be analyzed in order to determine the elasticity limit values (e.g.,T value). In some embodiments, the elasticity values are similar (orassumed to be similar) across all the stores. By definition, theelasticity is how fast a graph approaches its asymptote and therefore,this value may be assumed similar for all similar stores within anenterprise. In other words, a graphical representation of sales andspace for a particular product is assumed to have the similar overallshape and different values. For example, the shape of the graphrepresenting sales versus space in a grocery store in New York City isassumed to be similar to the shape of the graph representing salesversus space in rural Nebraska. The two graphs, however, may havedifferent space and sales values as the store in New York City may havea higher sales volume than the store in rural Nebraska. The analyticsserver may use existing and empirical data associated with other storeswithin the enterprise to determine the elasticity values. In someembodiments, the analytics server may determine the upper limit of theelasticity value (e.g., T_(max)) using the following formula:T _(max)=3×(98th percentile of spaces allocated by other stores withinthe enterprise as indicated by the sales and space dataset).

Where T_(max) represents the largest amount of space allocated to theproduct, which would yield the maximum amount of sales. In other words,the analytics server may determine the 98th percentile of the overallspace allocated to a product within each store within the sales andspace dataset (e.g., all the stores within the enterprise that offersimilar products) and determine the T_(max) by multiplying that numberby a factor of 3 or 3.5.

Similarly, the analytics server may calculate the minimum elasticityvalue (e.g., T_(min)) using the following formula:T _(min)=0.922×(2^(nd) percentile of space allocated by other storeswithin the enterprise as indicated by the sales and space dataset).Where T_(min) represents the least amount of space allocated to theproduct, which would yield the minimum amount of sales. In other words,the analytics server may determine the 2^(nd) percentile of the overallspace allocated to a product within each store within the sales andspace dataset (e.g., all the stores within the enterprise that offersimilar products) and determine the T_(min) by multiplying that numberby a factor of 0.922.

While the multipliers to calculate the upper and lower bounds ofelasticity are shown as 0.922, 3, 3, or 3.5, a person skilled in the artwill appreciate that the analytics server may use any other multiplieror factor based on the stores within the enterprise to calculated saidbounds. In some embodiments, the analytics server may receive thefactors (e.g., 0.922 for the lower elasticity limit and 3.5 for theupper elasticity limit) from the client device. The analytics server mayprovide an option for the client to input different factors to calculatethe elasticity in the user interface. The analytics server maydynamically adjust an elasticity value associated with a second dataset,wherein the second dataset comprises second dataset data pointscorresponds to space allocated to the product and the productperformance value associated with the first institution. The analyticsserver may generate a second dataset (e.g., a dataset representing thetarget store or the store for which the optimization is requested) anddynamically adjust the elasticity value, associated with the seconddataset, based on elasticity value calculated above (e.g., based onspace values of other stores within the enterprise).

At step 260, the analytics server may automatically and iterativelydetermine the elasticity value and the corresponding asymptote valuebased on the identified data points and the calculated elasticity range.The analytics server may iteratively calculate an elasticity value (andthe corresponding asymptote value) based on the identified data points(e.g., space and sales data points for the desired store) within theelasticity range. The analytics server may use the following formula tocalculate the elasticity and the asymptote values:

${K\left( {1 - e^{\frac{- {Space}}{t}}} \right)} = {Sales}$${K\left( {1 - e^{\frac{{- 2}*{Space}_{\max}}{t}}} \right)}>={{.99}*{K\left( {1 - e^{\frac{{- 2}*{Space}_{\max}}{t}}} \right)}}>={.99}$${.01}>=e^{\frac{{- 2}*{Space}_{\max}}{t}}$${\log{.01}}>=\frac{{- 2}*{Space}_{\max}}{t}$$t<=\frac{{- 2}*{Space}_{\max}}{\log{.01}}$

Where K represents the asymptote value and T represents the elasticityvalue. In an embodiment, the analytics server may calculate (asillustrated above) an elasticity range (t_(min)−t_(max)) and unbounded Kand T values (e.g., without considering t_(min)−t_(max)) and find acurve, which best fits the data points without any bounds orlimitations. If the unbounded T value is within the elasticity range(e.g., t_(min)−t_(max)), the analytics server may accept the T value(and the corresponding K value) and the process may end. However, if theT value is outside the range of t_(min)-t_(max), the analytics servermay dynamically adjust the T value to reflect the elasticity bound. Forexample, if t_(min) is determined to be 50, t_(max) is calculated to be150, and unbounded T value is calculated to be 190, the analytics servermay dynamically adjust/reduce the T value to t_(max) (e.g., 150). Inother embodiments the analytics server may adjust the T value to reflectthe closest bound. For example, a calculated unbounded T of 10 may beadjusted to 50 based on t_(min). The analytics server may theniteratively calculate a bounded T value and a corresponding asymptotevalue (e.g., K value), based on the new elasticity range (e.g., theanalytics server may calculate the asymptote value based on all the Tvalues within the new range of (t_(min)−t_(max))). In other words, theanalytics server may first calculate an unbounded K and T values, boundthe data points in response to the T value being outside the elasticityrange, and iteratively recalculate a bounded elasticity and asymptotevalue.

The analytics server may determine which K and T values are the best fit(e.g., represent the asymptote and elasticity) by determining the salesincrease for values higher than K. For example, if the K valuecalculated is 25 feet, the analytics server may determine the salesvolume for 25 feet (S_(25ft.)) and sales volume for 26 feet (S_(26ft.));the increase from S_(25ft.) to S₂₆ ft may not be higher than athreshold, which indicates that K value (e.g., 25 feet) is a trueasymptote. The threshold may be a pre-set value or may be received, bythe analytics server, from the client device. The analytics server mayiteratively recalculate the K value based on the upper and lowerelasticity range values and the increment value. In some embodiments,the increment value may be a pre-set value or may be received from theclient device. The analytics server may iteratively repeat thecalculation until the asymptote value is within the adjusted elasticityrange for the target store (e.g., elasticity range within the seconddataset). For example, if t_(min)=500, t_(max)=1500, and the incrementelasticity value is set to 1, the analytics server may iterativelycalculate 1000 different K and T values, according to theabove-mentioned formula.

At step 270, the analytics server may generate an optimized spacedataset comprising all the data points within the optimized sales vs.space graph (t_(min), t_(max), T, and k values). The analytics servermay also generate a graphical representation of the data points withinthe optimized dataset (second dataset) configured to display andillustrate an optimization trend. The analytics server may use varietyof regression modeling to determine a best space vs. sales trend. Insome embodiments, the graphical representation of the optimized datapoints may be accomplished by regression analysis and modeling.Regression analysis, as used herein, is a statistical process forestimating the relationships among variables (e.g., sales and spacevariable). Regression analysis helps one understand how the typicalvalue of the dependent variable (e.g., sales volume) changes when theindependent variable (e.g., space) is varied. The trend, as used herein,is a graphical representation of the regression function of theindependent variables. The analytics server may also generate anotification and display the trend on a user computing device. In someembodiments, the analytics server may generate a graphical userinterface (or update the information of an existing user interface) inorder to display the space dataset. The analytics server may modify thedata points based on the overall diminishing return curve (as explainedabove) and generate a new/modified optimized space dataset. Theanalytics server may also generate a spreadsheet including differentdata points within the optimized space dataset and display saidspreadsheet on the client-computing device.

FIG. 5 illustrates distribution of sales in relation to space datapoints, according to an embodiment. The analytics server may graphicallyrepresent the regression model or the optimized space dataset on theclient device (or a user interface associated with the client device).Graph 510 represents a conventional regression model that is calculatedusing conventional methods and without the benefit of a softwaresolution described within the present disclosure (e.g., without abounded and limited elasticity model). Graph 510, similar to graph 410of FIG. 4, represents undesirable and unrealistic data because graph 510has a small elasticity value, which yields an unrealistic asymptotevalue represented by data point 530. The analytics server may optimizethe sales and space dataset and generate a more realistic graph with alarger elasticity value (e.g., graph 520), which has an asymptote valuerepresented by data point 540. In this embodiment, the graph 520 isgenerated, by the analytics server, using a regression modeling (e.g.,regression line representing all the data points shown in FIG. 5) andalso represents a trend for projecting sales values give differentspaces allocated to product X.

In some embodiments, the analytics server may account for otherproducts, items, and departments while optimizing the space (e.g.,optimizing a combination of products). Optimizing one product at a timemay not be ideal for some stores (or departments within a store). Forexample, when the space allocated to one item is optimized andincreased, the space allocated to other items must be reduced because ofthe confined nature of shelving space within the store. This reductionmay not desirable due to a variety of factors. For example, the price orthe profitability of the second item may be higher or more desirablethan the first item. As a result, optimizing/increasing the spaceallocated to the first item may result in an overall decrease inprofits. In another example, many stores may experience temporary orpermanent demand increases due to a variety of factors, such as thestore's physical location and/or seasonal demands. For example, a clientwith a store near a baseball stadium may need to allocate certainportions of the store to baseball memorabilia and baseball-relatedproducts. The analytics server may account for multiple items in orderto generate an optimized store space comprising of different (or in someembodiments all the items) within the store.

In some embodiments, elasticity limits may be defined by the user orcalculated based on criteria received from the user device (e.g., othersimilar stores). The elasticity limits can be defined as values thathelp limit the optimization of asymptote and elasticity in order tosave/reduce computing power and time. Calculating an asymptote withdifferent elasticity values is an intense process and may use computerresources because of the high number of combinations. For example, thenumber of combinations may be in thousands for one item within onedepartment and potentially infinite number of combinations givendifferent increments of the elasticity value for each K value. Theanalytics server may drastically reduce the time and computationalresources spent by limiting this combination within the confines of theelasticity limit.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

Embodiments implemented in computer software may be implemented insoftware, firmware, middleware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the invention.Thus, the operation and behavior of the systems and methods weredescribed without reference to the specific software code beingunderstood that software and control hardware can be designed toimplement the systems and methods based on the description herein.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed herein may be embodied in a processor-executable softwaremodule, which may reside on a computer-readable or processor-readablestorage medium. A non-transitory computer-readable or processor-readablemedia includes both computer storage media and tangible storage mediathat facilitate transfer of a computer program from one place toanother. A non-transitory processor-readable storage media may be anyavailable media that may be accessed by a computer. By way of example,and not limitation, such non-transitory processor-readable media maycomprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othertangible storage medium that may be used to store desired program codein the form of instructions or data structures and that may be accessedby a computer or processor. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and Blu-ray disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media. Additionally, the operations of a method oralgorithm may reside as one or any combination or set of codes and/orinstructions on a non-transitory processor-readable medium and/orcomputer-readable medium, which may be incorporated into a computerprogram product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the following claims and theprinciples and novel features disclosed herein.

While various aspects and embodiments have been disclosed, other aspectsand embodiments are contemplated. The various aspects and embodimentsdisclosed are for purposes of illustration and are not intended to belimiting, with the true scope and spirit being indicated by thefollowing claims.

What is claimed is:
 1. A method comprising: retrieving, by a server, aplurality of datasets having a set of data points, each data pointcorresponding to an independent variable corresponding to a spaceallocated to a product and a dependent variable corresponding to aproduct performance value associated with the product, wherein eachdataset within the plurality of datasets corresponds to an entity;automatically calculating, by the server, an elasticity range based onthe space allocated to the product, wherein the elasticity rangerepresents a maximum elasticity range value and a minimum elasticityrange value each representing maximum and minimum space allocated to theproduct associated with the plurality of datasets; determining, by theserver, an unbounded elasticity value and a corresponding asymptotevalue based on a first dataset within the plurality of datasets, whereinthe first dataset comprises data points corresponding to space allocatedto the product and the product performance value associated with theproduct within a first institution; dynamically adjusting, by theserver, the unbounded elasticity value based on the elasticity range, inresponse to the unbounded elasticity value being outside the elasticityrange; iteratively calculating, by the server, a bounded asymptote valueand a bounded elasticity value based on the data points of the firstdataset until the bounded value and bounded elasticity values are withinthe adjusted elasticity range, wherein with each iteration, the serverincreases the bounded asymptote value by an incremental value until thebounded asymptote value is within the adjusted elasticity range; anddynamically displaying, by the server, an optimized space trendcomprising plurality of data points of the first dataset arranged inaccordance with the bounded asymptote value and the bounded elasticityvalue.
 2. The method of claim 1, wherein the server identifies a bestspace value to be allotted to the product based on the bounded asymptotevalue and a bounded elasticity value.
 3. The method of claim 1, whereinthe minimum elasticity range value is calculated by multiplying a secondpercentile of space allocated to the product within a plurality ofspaces allocated to the product within each entity by a firstmultiplier.
 4. The method of claim 3, wherein the first multiplier isabout 0.922.
 5. The method of claim 3, wherein the first multiplier isreceived, by the server.
 6. The method of claim 1, wherein the maximumelasticity range value is calculated by multiplying 98th percentile ofspace allocated to the product within a plurality of spaces allocated tothe product within each entity by a second multiplier.
 7. The method ofclaim 6, wherein the second multiplier is about
 3. 8. The method ofclaim 6, wherein the second multiplier is received, by the server. 9.The method of claim 1, wherein the incremental value is received, by theserver.
 10. The method of claim 1, wherein the server further generatesa spreadsheet corresponding to the optimized space trend.
 11. A computersystem comprising: a plurality of computing devices, each computingdevice associated with an entity, each computing device configured tostore a plurality of datasets having a set of data points, each datapoint corresponding to an independent variable corresponding to a spaceallotted to a product and a dependent variable corresponding to aproduct performance value associated with the product; a server incommunication with the plurality of computing devices, the serverconfigured to: retrieve the plurality of datasets; automaticallycalculate an elasticity range based on the space allocated to theproduct, wherein the elasticity range represents a maximum elasticityrange value and a minimum elasticity range value each representingmaximum and minimum space allocated to the product associated with theplurality of datasets; determine an unbounded elasticity value and acorresponding asymptote value based on a first dataset within theplurality of datasets, wherein the first dataset comprises data pointscorresponding to space allocated to the product and the productperformance value associated with the product within a firstinstitution; dynamically adjust the unbounded elasticity value based onthe elasticity range, in response to the unbounded elasticity valuebeing outside the elasticity range; iteratively calculate a boundedasymptote value and a bounded elasticity value based on the data pointsof the first dataset until the bounded value and bounded elasticityvalues are within the adjusted elasticity range, wherein with eachiteration, the server increases the bounded asymptote value by anincremental value until the bounded asymptote value is within theadjusted elasticity range; and dynamically display an optimized spacetrend comprising plurality of data points of the first dataset arrangedin accordance with the bounded asymptote value and the boundedelasticity value.
 12. The computer system of claim 11, wherein theserver identifies a best space value to be allotted to the product basedon the bounded asymptote value and a bounded elasticity value.
 13. Thecomputer system of claim 11, wherein the minimum elasticity range valueis calculated by multiplying a second percentile of space allocated tothe product within a plurality of spaces allocated to the product withineach entity by a first multiplier.
 14. The computer system of claim 13,wherein the first multiplier is about 0.922.
 15. The computer system ofclaim 13, wherein the first multiplier is received, by the server. 16.The computer system of claim 11, wherein the maximum elasticity rangevalue is calculated by multiplying 98th percentile of space allocated tothe product within a plurality of spaces allocated to the product withineach entity by a second multiplier.
 17. The computer system of claim 16,wherein the second multiplier is about
 3. 18. The computer system ofclaim 16, wherein the second multiplier is received, by the server. 19.The computer system of claim 11, wherein the incremental value isreceived, by the server.
 20. The computer system of claim 11, whereinthe server further generates a spreadsheet corresponding to theoptimized space trend.